Speaker
Description
In various settings goodness of fit tests are constructed based on processes which under a null hypothesis of interest converges to a Brownian bridge. Examples include classical testing of distributional assumptions, testing goodness of fit in time series models, finance model testing, choosing hyper parameters in machine learning and also testing for trend in recurrent event data. Tests are then constructed based on various ways of measuring deviation from Brownian bridge behavior, and either asymptotic null distributions or resampling methods are used to approximate the distribution of the test statistic.
In this talk I will discuss various measures of deviation from Brownian bridge, some classical and some more recent suggestions, and consider their properties. Some illustrations will be shown for applications in trend testing.